GenAI Development LEAD

AI Software Development Lead (AI-Assisted Development, GenAI, Agentic AI)

Experience: 10+ years

Location: [Pune/Hyderabad]

Seniority Level: Lead / Principal IC

About the Role

AI Software Development Lead

Will champion vibe codingthe emerging practice of using LLMs and coding agents (e.g., GitHub Copilot, Cursor, Claude Code, etc.) to generate working code from natural-language instructions, iterating rapidly while enforcing quality and compliance. Your leadership will modernize engineering workflows and scale AI-first development practices across diverse BFSI portfolios.

Will architect and deliver enterprise-grade AI applications leveraging Generative AI (GenAI), Agentic AI, LLMs, RAG, and Agentic RAGwith a strong focus on security, governance, observability, and cost efficiency.

This role operationalizes AI-first delivery, increases developer productivity, strengthens proposal win rates through compelling AI solutioning, and ensures secure, compliant implementations aligned with BFSI standards.

Key Responsibilities

1. AI-Assisted Development Leadership

a. Drive organization-wide adoption of coding agents and vibe coding practices; define guardrails, standards, and governance for BFSI environments.

b. Build playbooks for prompt engineering, code generation, refactoring, test generation, documentation, and secure patterns using Copilot/Cursor/Claude Code, etc.

c. Deliver enablement programs: workshops, hands-on labs, brown-bags; establish usage analytics and productivity KPIs.

2. Solutioning, Pre-Sales & Proposal Support

a. Partner with sales, pre-sales, service lines, and delivery to:

• tailor AI-first roadmaps, demo assets, and POCs/Pilots
• lead technical solutioning for RFPs/RFIs: author architecture options, reference designs, delivery models, and cost estimates.
• Create client-facing proposals with clear business outcomes, risk/compliance alignment, and measurable success metrics.

3. Architecture & Delivery (LLMs, RAG, Agents)

a. Architect and deliver agentic systemstool orchestration, planning/critique loops, memory, multi-agent collaboration for complex BFSI workflows.

b. Own end-to-end solutioning: data acquisition/transform; embeddings/retrieval; prompt pipelines; function calling/tool schemas; APIs/SDKs; UI integration.

4. RAG & Agentic RAG Best Practices

a. Design advanced RAG pipelines: chunking, hybrid retrieval (vector + keyword), rerankers, query rewriting, context compression, caching, grounding, and citations.

b. Build Agentic RAG flows combining retrieval + tool use + planning loops to maximize accuracy, policy adherence, and cost performance.

5. Quality, Evals & Observability

a. Define LLM/agent evaluation: groundedness, factuality, precision/recall, hallucination rate, agent success rate, latency, cost/query.

b. Implement observability: tracing, token/cost accounting, prompt/version lineage, user feedback loops, and red-team logs.

6. Collaboration & Leadership

a. Mentor engineers; lead design reviews and AI SDLC standards; influence architecture councils.

b. Drive build-vs-buy decisions, vendor evaluations, and cost/latency optimization strategies.

Required Qualifications

• 1012+ years in software engineering, including enterprise architecture and delivery; 3+ years hands-on with LLMs/GenAI.
• Proven BFSI exposure (banking, payments, insurance, capital markets, fintech) with security/compliance constraints.
• Strong software engineering fundamentals and coding in Python plus one of Java/TypeScript/C#/Scala; production APIs/microservices; CI/CD.
• Hands-on with coding agents & IDEs: GitHub Copilot, Cursor, Claude Code etc. and IDE integrations (VS Code/IntelliJ/JetBrains); expert in vibe coding workflows.
• Deep knowledge of LLM ecosystems (e.g., Azure OpenAI/OpenAI, Anthropic, Google, Meta): prompting, function calling, MCP (Model Context Protocol), token/cost management.
• Expertise in RAG: vector DBs (FAISS, Pinecone, Milvus, pgvector/Postgres, Elastic/OpenSearch), embedding strategies, and rerankers.
• Experience with agent frameworks: LangGraph, AutoGen, Sematic Kernel, CrewAI (or similar) and tool integrations.
• LLMOps and eval tooling: LangSmith, TruLens, Ragas, DeepEval, W&B, MLflow; prompt caching/compression; distillation.
• Infra & data: Docker/Kubernetes, Azure/AWS/GCP, Kafka, Airflow; API security and secrets management.
• Testing & quality: unit/integration/e2e, canary/blue-green, A/B or interleaving experiments for AI features.
• Excellent communication; able to translate BFSI needs into reliable AI systems with clear KPIs.

Education & Certifications

• Bachelors/Masters in Computer Science, Software Engineering, Data/AI, or related field (or equivalent experience).
• Preferred: Cloud architect/ML/AI certifications (Azure/AWS/GCP)